Nearly 3 of the top 5 AI-recommended brands change once you leave New York and English

Nearly three of the top five brands that ChatGPT and Gemini recommend change once a buyer moves away from New York and English, according to a September 2026 study from marketing firm FTA Global. The report ran 1,200 identical purchase questions across 10 business categories in 12 cities and seven languages, then repeated each question five times to separate a real geographic pattern from ordinary answer variation. Of 206 brands that appeared across every test, 159 showed up in only one country. For a brand tracking a single global AI Share of Voice score, that gap sits entirely outside the dashboard.

What FTA Global's Context Report actually tested

FTA Global's Context Report tested 1,200 identical purchase questions in 10 business categories, including CRM, payroll, accounting, payments, and HR and recruiting, run across 12 cities and seven languages on ChatGPT and Gemini, then repeated five times each to confirm the pattern was not just run-to-run noise.

Payment software shows the pattern most clearly. Asked which payment gateway to use from a New York, English-language account, both models named Stripe, Square and PayPal. Asked the identical question from Mumbai, the answer became Razorpay, Cashfree and PayU, a list that shared not one brand with the New York result.

Why payment and HR brands swing more than everyday software

Not every category moved by the same amount. Payment-related categories averaged 4.4 of the top five recommended brands changing across the 12 cities, compared with 1.5 for everyday categories such as video conferencing and cloud storage, and no top US HR software brand appeared in any other country tested.

That split tracks how local the underlying vendor landscape actually is. A payment processor has to connect to a country's own banks, card networks and currency rules, and an HR platform has to handle that country's payroll tax and employment law, so the correct answer genuinely changes from one market to the next. A project management tool or a video conferencing app works close to identically everywhere, so geography has less to change.

ChatGPT and Gemini do not even agree with each other

Location was not the only source of disagreement. Tested on the identical question in the identical city, ChatGPT and Gemini shared only about 3.2 of the top five recommended brands on average, and running the same query five times in a row produced the same top brand in just 35% of trials.

That cross-model gap matches a pattern a separate BrightEdge analysis of five AI engines already found: the engines agreed on which brands to recommend only 36% to 55% of the time, with the overlap dropping even lower in categories like healthcare.

A brand that looks strong in ChatGPT and barely visible in Gemini is not necessarily losing ground. It may simply be seeing one engine's version of an answer that was never going to match the other's.

Academic research backs up the pattern

The geographic effect is not unique to FTA Global's data. A paper posted to arXiv in April 2026 built LocQA, a benchmark of 2,156 questions across 12 languages, and tested it against 32 language models to expose hidden geographic and cultural defaults.

LocQA's results showed that when a question's locale was left ambiguous, models fell back on default assumptions about which country's laws, dates, units of measurement or institutions applied, assumptions that did not always match the language the question was asked in. That lines up with what FTA Global measured: ChatGPT and Gemini default toward answers shaped around their biggest, best-documented market, English-speaking and American, and only partly adjust once a different country or language is specified.

Why a single AI Share of Voice score hides all of this

A single global AI Share of Voice number cannot show which five cities or which three languages it was actually measured in, and most brands never ask. Semrush expanded its own AI visibility prompt database to 32 countries in May 2026, because, in the company's words, AI search is not a single-market phenomenon.

The expansion added 17 new regional markets, from Argentina to Saudi Arabia, to a database Semrush says now holds 261 million prompts. It is a tacit admission from one of the larger AI visibility vendors that a brand's real exposure in AI answers can look nothing like its headline score once the city or the language changes. The formula for measuring AI Share of Voice works the same way in every market; what changes is which prompts, cities and languages you run it against, and most brands still run it against exactly one.

It is a familiar shape of blind spot. The same instinct that produces one global score is also what leaves trackers skipping Mistral and DeepSeek entirely, or keeps them weighting every engine equally after ChatGPT's US prompt share dropped 20 points in 2026.

What to actually test before trusting one score

The fix is not a new metric, it is a wider test. Running the same purchase questions in the cities and languages where actual buyers search, rather than relying on English-only testing from one location, is what exposed the gap in the first place.

Categories with heavier local regulation, payments, HR, insurance, financial services, deserve market-by-market checks before a brand trusts a blended score for them. Everyday software categories can likely stay on a lighter testing schedule, since FTA Global's data showed far less movement there. In Germany, where the same study found that AI engines pulled 42% of their cited sources from German-language web pages, a brand with no German-language content is working against the local source pool before a single ranking factor comes into play.